Several wireless communication technologies, e.g. WCDMA, LTE or GSM, deploy communication channels shared between multiple mobile users in time. In the present description, the technical ideas will primarily be presented in the context of High Speed Downlink Shared Channel (HS-DSCH) which is the key data channel for High Speed Downlink Packet Access (HSDPA) radio interface in a WCDMA telecommunication system [1][4] but the ideas may also be applied in LTE, GSM or any other wireless communication system where data is transmitted over a shared channel.
In such a system, shown by way of example in FIG. 1, a radio base station 10 transmits data on the shared channel, such as HS-DSCH in WCDMA, to several mobile terminals, also referred to as mobile users, 20-1, . . . , 20-5, where terminals may be mobile phones, computers or other devices with a built-in mobile receiver and/or transmitter. There may be many active connected terminals served by the same base station, where connected means that the terminals have control and data communication channels including the shared data channel established towards this base station. FIG. 1 is a schematic diagram illustrating scheduling of common transmission resources on a shared channel.
In this example, base station resources for the shared channel such as HS-DSCH, are shared between all users in one cell. Consequently, the base station may transmit data on the shared channel such as HS-DSCH to one or a few terminals at a time only. To be able to serve all active terminals in the cell, it transmits data in small intervals or time slots and chooses new terminals for transmission at each time slot. In other words, the data channel is on for users granted access to the shared channel at a particular time slot, and the data channel is off for users that are not granted access at that time slot. The decisions regarding to which users to transmit are made by the scheduler function in the base station. The scheduler function keeps track of available resources and at each time slot grants access to the shared communication channel to selected users according to a certain long-term strategy [4]. The decisions typically take into account the base station resources allocated for the shared channel such as HS-DSCH and the feedback from the terminals about experienced (HS-DSCH) radio link quality, e.g. in the form of Channel Quality Indicator (CQI) [1].
The question addressed here is how to define a short-term user selection strategy at each time slot that would be able to achieve certain predefined performance requirements over a longer time period under the further condition that traffic to different users may have different performance demands or so called Quality of Service (QoS) guarantees. Compliance to desired guarantees of this kind is becoming a crucial feature for downlink mobile broadband performance as the variety and number of supported services is growing all the time.
A difficulty in finding a strategy that achieves QoS demands over a longer time scale stems from the fact that the scheduler usually may not exactly predict the amount of data that may be transmitted, or transmission delays that may be experienced over the time period that it is operating over. The reason is that quality of radio communication channels and hence the amount of data each user is able to receive at each time slot is rapidly varying in time and between different users. Hence, the only way to achieve predefined performance guarantees for each traffic class is by selecting appropriate users for transmission at each time slot, so-called user scheduling.
The service performance guarantee considered here is dynamic bandwidth allocation that follows given bandwidth target shares, i.e. QoS requirements on bandwidth sharing. By this guarantee, each traffic priority class is assigned a bandwidth share ratio relative to the other classes. The whole transmitted data capacity, which may be varying in time, is then dynamically divided between these classes according to these share ratios. The term “bandwidth share” refers here to a transmitted data rate as a share of the total system capacity. Similar QoS requirements may also be stated on the transmission delays.
FIG. 2 is a schematic diagram illustrating an example of QoS bandwidth sharing between different traffic priority classes according to the target share ratios 4:2:1.
An example of the data transmission in a communication system that complies to the QoS requirements on bandwidth sharing considered here is depicted in the example of FIG. 2. In this example, three user priority classes should share the bandwidth according to the target ratios 4:2:1. This means that data traffic to the three user classes shall get shares of 4/7, 2/7 and 1/7 of the total system capacity wherever there are users of all three classes in the system. Otherwise, they should divide the system capacity according to other corresponding bandwidth ratio combinations. In the first time period shown in FIG. 2, there are only users receiving priority 3 class data in the system. They get the whole bandwidth, i.e. the data rate is equal to the total system capacity. Then, in the next period, some users with priority 2 class traffic join the system. The bandwidth is then divided between the data of the two classes by the ratio 2:1, i.e. priority class 2 data gets twice as much bandwidth as priority class 3 data, or, 2/3 and 1/3 of the total system capacity. Then, in the last time period, new users with priority 1 data arrive and hence the bandwidth is shared according to the ratios 4:2:1 or bandwidth shares 4/7, 2/7 and 1/7.
One widely used solution to achieve given bandwidth shares for different traffic priority classes is to apply a scheduling strategy based on user ranking with additional weights for service priority. Some known user ranking-based strategies are e.g. Round Robin, Maximum C/I or Proportional Fair [3] and [4]. In such strategies, each user is assigned the scheduling priority score or rank at every time slot based on e.g. users' instantaneous radio link quality, delay elapsed since the last transmission, amount of data in the buffer [8] or some other information. Users with the highest scheduling score or rank are selected for transmission.
Adding the supplementary service priority weight to a ranking-based strategy guarantees that users transmitting traffic with higher priority are selected for transmission more often while still maintaining the general chosen scheduling strategy within each traffic class. Typically, this weight is chosen to be proportional to the target share ratio for the users' class. For example, given two traffic priority classes with data rate target ratios 2:1, users in the class with the larger target data rate would get a scheduling priority weight two times as high as the weight for users in the other class. Then given that a primary scheduling strategy is chosen such that both user classes would get the same average long-term data rate if no priority weights were applied, users in the class with the larger priority would be chosen for transmission twice as often and hence would achieve twice as high data rate.
To be able to get the same average data rate for both classes, a scheduling strategy has to possess some fairness property, i.e. it has to give equal access to the system to all the users in certain sense. Examples of fair scheduling strategies include Equal Rate strategy which guarantees the same long-term average data rate to all users or Round Robin strategy that assigns all users equal amount of transmission slots. Proportional Fair strategy provides users with data rate proportional to their long-term average radio link quality [3][5] and hence is also a feasible strategy to be deployed with predefined priority weights under the further condition that all user classes would experience the same long-term average radio link quality.
Predefined service priority weights added to the ranking-based strategies with high level of fairness, like Equal Rate or Round Robin, are known to provide required bandwidth shares but they lead to low total amount of data transmitted by the system. The reason is that these strategies ignore the radio link quality and hence may allocate transmission resources to the users that at that time slot experience a temporarily fading dip in signal power. This is a consequence of the fairness-system capacity dilemma in the radio communication systems according to which it is impossible to maximize both of these factors at the same time.
Proportional Fair strategy is known to yield an acceptable compromise between the level of fairness and the total system capacity in case no traffic differentiation is needed [3] and [2]. However, deployed together with predefined service priority weights it yields the required bandwidth share to the different traffic priority classes only under condition that the long-term average radio link quality for each user generating traffic in a certain traffic priority class is not changing over time and that the service priority weights are adjusted to this long-term average quality.
This condition is often violated in the real radio communication networks. There, users come and leave the system randomly and are moving around while connected. The users may traverse coverage dips or experience sudden interference peaks from other cells, which results in that even the long-term average radio link quality may vary widely in time and between different users. As a consequence, Proportional Fair strategy with predefined service priority weights may often fail to achieve the targeted bandwidth shares.
Furthermore, a solution based on a strategy with the same or lower level of fairness as Proportional Fair and predefined service priority weights is typically not robust. That is, a small deviation in average radio link quality between users in different classes may result in a large violation of their bandwidth share ratios. E.g. if a user from a higher priority class has lower average radio link quality than the other users, he/she will be selected for transmission more seldom than needed and will hence get a lower bandwidth share than the target.
These two problems are illustrated by computer simulation results shown in the example of FIG. 3.
FIG. 3 is a schematic diagram illustrating a simulated example for Proportional Fair scheduling policy with constant service priority weights in case of three moving users with different priorities. Priority 1 user moves to a position where average SIR gets 10 dB better and then to a position where average SIR gets 10 dB worse but the bandwidth shares provided by the algorithm fail to adapt to the changes.
There are three users in the system receiving data from three different traffic priority classes with target bandwidth shares 4:2:1. In the beginning, all users have the same average radio link quality and hence Proportional Fair strategy with constant service priority weights renders the required bandwidth shares. However, the user receiving priority class 1 data moves to a position where the average SIR experienced by that user becomes 10 dB higher than that of the other two users and then to a position where his/her average SIR becomes 10 dB lower than that of the other two users. As a consequence, Proportional Fair strategy with constant service priority weights fails to provide required bandwidth shares. As may be seen in the example of FIG. 3, the received data rate of the moving user gets changed while the rates of the other two users remain almost constant thus violating the bandwidth shares.
Another drawback of the strategies with constant service priority weights is that it may take too long time to achieve the target bandwidth share for users with small amount of data in the transmission buffers. This makes it not very suitable for many popular communication services, e.g. web browsing, that generate data in bursts, i.e. where arrivals of many relatively small data chunks are followed by longer periods of waiting times.
A different approach, which does not consider bandwidth share ratios, is taken in reference [10], where a scheduler for a shared wireless channel guarantees QoS requirements for different traffic classes given by explicit minimum target throughputs. However, this puts a requirement on the total system throughput, which needs to be large enough to support the guaranteed throughput bit rates. A token count tracking the user's achieved performance relative to the target minimum throughput is determined for each user, and a weight is determined for each user based on one or more of the token count and a current rate requested by the user. A user having the highest weight is scheduled to be served the next transmission.
Reference [11] relates to a system and method for dynamically controlling weights assigned to consumers competing for a shared resource in which a weighted proportional-share scheduler schedules access of competing flows to a computing service. Reference [12] also relates to weighted proportional share scheduler that maintains fairness in allocating shares of a common resource. The total service capacity of the shared resource in [11, 12] is constant in time but shares required by the users are change by a controller according to the workload of the system in order to achieve the required amount of service for each user. The state of the system is changing due to varying system workload, i.e. flows coming into and leaving the system. These solutions are not useful for wireless communication systems since they do not consider the fact that the total service capacity of a shared resource in the form of a shared radio channel in a wireless system is varying rapidly in time due the radio link quality variations, which introduce new states to the overall system, but rather assume that the service capacity is constant.
Reference [13] relates to a dynamic fair resource allocation scheme for the uplink of a Wideband Code Division Multiple Access (WCDMA) cellular network, taking into account the characteristics of channel fading and inter-cell interference. For non-real-time traffic without stringent delay requirements, the assigned weights can be dynamically adjusted according to channel conditions and, in particular, knowledge of the multipath fading channel gains between the mobile users and their home base station.